Simulation of the electromagnetic response of the human body relies heavily upon efficient computational CAD models or phantoms. The Visible Human Project (VHP)-Female v. 3.1 - a new platform-independent full-body electromagnetic computational model is revealed. This is a part of a significant international initiative to develop powerful computational models representing the human body. This model’s unique feature is full compatibility both with MATLAB and specialized FEM computational software packages such as ANSYS HFSS/Maxwell 3D and CST MWS. Various mesh processing algorithms such as automatic intersection resolver, Boolean operation on meshes, etc. used for the development of the Visible Human Project (VHP)-Female are presented. The VHP - Female CAD Model is applied to two specific low frequency applications: Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS). TMS and tDCS are increasingly used as diagnostic and therapeutic tools for numerous neuropsychiatric disorders. The development of a CAD model based on an existing voxel model of a Japanese pregnant woman is also presented. TMS for treatment of depression is an appealing alternative to drugs which are teratogenic for pregnant women. This CAD model was used to study fetal wellbeing during induced peak currents by TMS in two possible scenarios: (i) pregnant woman as a patient; and (ii) pregnant woman as an operator. An insight into future work and potential areas of research such as a deformable phantom, implants, and RF applications will be presented.

Yes / The development of the patchwise Partial Di erential Equation (PDE) framework a few years a- go has paved the way for the PDE method to be used in mesh signal processing. In this paper we, for the rst time, extend the use of the PDE method to progressive mesh compression and mesh denoising. We, meanwhile, upgrade the existing patchwise PDE method in patch merging, mesh partitioning, and boundary extraction to accommodate mesh signal processing. In our new method an arbitrary mesh model is partitioned into patches, each of which can be represented by a small set of coe cients of its PDE spectral solution. Since low- frequency components contribute more to the recon- structed mesh than high-frequency ones, we can achieve progressive mesh compression and mesh denoising by manipulating the frequency terms of the PDE solution. Experimental results demonstrate the feasibility of our method in both progressive mesh compression and mesh denoising.

Many architecure, medical and engineering applications need a spacial support for various numerical computations (i.e. FEM simulations). Tetrahedral meshes are one of perspective spatial representations for them. In this thesis, several possibilities of effective tetrahedral mesh representation for its generating and processing are described. A computer library for the mesh processing is proposed which can be characterized by memory  efficient imposition of the mesh while preserving the ability to apply topological and geometric algorithms effectively. The library is implemented in C++ language using templates. Time and space complexity of typical mesh operations is compared with CGAL library and according to measurements the proposed library has lower memory requirements than CGAL.

Understanding of the shape and size of different features of human body from the scanned data is necessary for automated design and evaluation of product ergonomics. The traditional method of finding required body dimensions by manual measurements (Anthropometry) has many sociological, logistical and technical drawbacks such as prolonged time, skilled researcher for consistency and accuracy of measurements, undesirable physical contact between the subject and the researcher, required presence of people from different demographic categories or travel of researcher with equipments. If these di- mensions are extracted from the stored digital human models, above drawbacks can be eliminated. With the emergence of laser based 3d scanners, it is now possible generate a large database of surface models of humans from different demographic backgrounds but the automatic processing of 3d meshes is under development. Though some commercial packages are available for extraction of a limited number of dimensions from full body scans, mostly belonging to topologically separable body parts like hands and legs, the dimensions associated with head and face are particularly not available in public domain. The processing of surface models of head and face from the automatic measurement point of view is also not discussed in literature though this type of data has many practical applications like ergonomic design of close-fitting products like respiratory masks,ophthalmic frames (spectacles), helmets and similar head-mounted devices; Creation of a facial feature database for face modeling coding and reconstruction and for use in forensic sciences; Automated anthropological surveys and Medical growth analysis and aesthetic surgery planning. Hence, in this thesis, a computational framework is developed for automatic detection, recognition and measurement of important facial features namely eyes, eyebrows, nose, mouth and moustache (if applicable) from scanned head and shoulder polyhedral models. After preprocessing the scanned mesh manually to fill holes and remove singular vertices, discrete differential geometric operators were implemented to compute surface normals and curvatures. Mean curvature magnitude was used as the primary metric to segment the mesh using morphological watershed algorithms which treat the mesh as a height map and separate the regions according to the water catchment basins. After visualization it was hypothesized that the important facial features consist of relatively high curvature regions and based on this hypothesis a much faster approach was then employed based on mathematical morphology to group the high curvature vertices into regions based on adjacency. The important feature regions isolated this way were then identified and labeled to be belonging to different facial features by a decision tree based on their relative spatial disposition. Adaptive selection of parameters was incorporated later to ensure robustness of this algorithm. Critical points of these identified features are recognized as the standard landmarks associated with those primary facial features. A number of clinically identified landmarks lie on the facial mid-line. An efficient algorithm is proposed for detection and processing of the mid-line using a point sampling technique which is fast and has immunity to noise in the data. An algorithm to find shortest path between two vertices while traveling along the edges is implemented to measure on-surface distances and to isolate the nose. Complete program comprising of curvature and surface normal computations, seg- mentation and identification of 6 important features, facial mid-line processing, detection of total 17 landmarks and shortest path computations to separate nose takes about 2 minutes to work including visualization on a full resolution mesh of typically 2,15,521 Vertices and 4,30,560 Faces. The algorithm was tested successfully on more than 40 faces with minor exceptions. The results match human perception. The computed measurements were also compared with the physical measurements for a few subjects, the measurements were found to be in good agreement and satisfactory for its usage in product ergonomics and clinical applications.

This thesis explores and explains a method using discrete curvature as a feature to find regions of vertices that can be classified as being likely to indicate the presence of an underlying tumor on a kidney surface mesh. Vertices are tagged based on curvature type and mathematical morphology is used to form regions on the mesh. The size and location of the tumor is approximated by fitting a sphere to this region. The method is intended to be employed in noninvasive radiotherapy with a dynamic soft tissue model. It could also provide an alternative to volumetric methods used to segment tumors. A validation is made using the images from which the kidney mesh was constructed, the tumor is visible as a comparison to the method result. The dynamic kidney model is validated using the Hausdorff distance and it is explained how this can be computed in an effective way using bounding volume hierarchies. Both the tumor finding method and the dynamic model show promising results since they lie within the limit used by practitioners during therapy.

La complexité de la forme de la surface est une caractéristique morphologique des surfaces pliées. Dans cette thèse, nous visons à développer des méthodes spectrales pour quantifier cette caractéristique du cortex cérébral humain reconstruit à partir d'images MR structurales. Tout d'abord, nous suggérons certaines propriétés qu'une mesure standard de la complexité de surface devrait posséder. Ensuite, nous proposons deux définitions claires de la complexité de la surface en fonction des propriétés de flexion de surface. Pour quantifier ces définitions, nous avons étendu la transformée de Fourier à fenêtres illustrée récemment pour transformer en maillage des surfaces. Grâce à certaines expériences sur les surfaces synthétiques, nous montrons que nos mesures basées sur la courbure permettent de surmonter les surfaces classiques basées sur la surface, ce qui ne distingue pas les plis profonds des oscillants ayant une surface égale. La méthode proposée est appliquée à une base de données de 124 sujets adultes en bonne santé. Nous définissons également la complexité de la surface par la régularité de Hölder des mouvements browniens fractionnés définis sur les collecteurs. Ensuite, pour la première fois, nous développons un algorithme de régression spectrale pour quantifier la régularité de Hölder d'une surface brownienne fractionnée donnée en estimant son paramètre Hurst H. La méthode proposée est évaluée sur un ensemble de sphères browniennes fractionnées simulées. En outre, en supposant que le cortex cérébral est une surface brownienne fractionnée, l'algorithme proposé est appliqué pour estimer les paramètres Hurst d'un ensemble de 14 corticus cérébraux fœtaux. / Surface shape complexity is a morphological characteristic of folded surfaces. In this thesis, we aim at developing some spectral methods to quantify this feature of the human cerebral cortex reconstructed from structural MR images. First, we suggest some properties that a standard measure of surface complexity should possess. Then, we propose two clear definitions of surface complexity based on surface bending properties. To quantify these definitions, we extended the recently introduced graph windowed Fourier transform to mesh model of surfaces. Through some experiments on synthetic surfaces, we show that our curvature-based measurements overcome the classic surface area-based ones which may not distinguish deep folds from oscillating ones with equal area. The proposed method is applied to a database of 124 healthy adult subjects. We also define the surface complexity by the Hölder regularity of fractional Brownian motions defined on manifolds. Then, for the first time, we develop a spectral-regression algorithm to quantify the Hölder regularity of a given fractional Brownian surface by estimating its Hurst parameter H. The proposed method is evaluated on a set of simulated fractional Brownian spheres. Moreover, assuming the cerebral cortex is a fractional Brownian surface, the proposed algorithm is applied to estimate the Hurst parameters of a set of 14 fetal cerebral cortices.